Algorithm-based recommendations are everywhere. Imagine you are browsing news articles on the website of the New York Times. You see a piece in the “Science” section, find it interesting, click on the title, and start reading. Once you finish the article, the webpage automatically generates other article recommendations for you so that you extend your engagement with the platform’s content. The recommendations are branded with the tagline: “More in Science,” the section you have already been reading.
While most companies provide explanations for why customers receive recommendations, they differ in the specific strategies they adopt. Some companies, like the aforementioned New York Times, emphasize that recommendations are item-based: That is, they are based on common attributes across products (e.g., “More in Science” by the New York Times, and “Similar to [what you have listened to]” by Spotify). In contrast, other companies highlight that their recommendations are user-based by focusing on the overlap in customer preferences (e.g., “Customers who viewed this item also viewed…” by Amazon and “Customers also watched…” by Netflix). Importantly, companies can explain the same recommendation as either item-based or user-based. Today’s recommender systems frequently adopt a hybrid approach that accounts for both common attributes across products and common preferences across customers.
A new study in the Journal of Marketing compares two different explanations for recommendations in terms of their effectiveness, providing marketers with tools to maximize this important engagement tool. We investigate which of the two explanations (hereafter referred to as item-based and user-based framings) is more effective at triggering clicks on a recommendation. We suggest that item-based and user-based framings differ in terms of the information they provide to customers regarding how a recommendation is made. Both framings tell customers that the recommendation is based on a product matching of the focal item that customers have shown interest in to the recommended item: Item-based framing matches products by their attributes, whereas user-based framing matches products by their consumers. Critically, user-based framing also suggests to customers that the recommendation is based on taste matching among users who shared interest in the focal item. By providing information on taste matching beyond product matching, user-based framing serves as a sort of “double guarantee” for customers liking the recommended product.
To test whether user-based framing outperforms item-based framing in terms of recommendation click-throughs, we conducted two field studies within WeChat, the top social media app in China. Numerous WeChat users subscribe to media accounts to receive daily news articles. We collaborated with a media company that publishes popular science articles and summaries of academic research on WeChat. We embedded a pair of recommendations at the end of each day’s focal article. One article was recommended using user-based framing (“People who like this article also like”) and the other using item-based framing (“More Analyses of Scientific Research” in one field study and “Similar to this article” in the other). In both studies, we find that user-based framing increased the click-through rates of recommended articles compared to item-based framing. When asking subscribers about their understanding of the two framings, we find that they see that both framings suggest product matching as the basis for recommendations, but that user-based framing also signals taste matching. This confirms that user-based framing provides additional information to customers.
However, customers do not always see taste matching as successful. When taste matching is perceived as inaccurate, user-based framing is no longer more advantageous than item-based framing or even becomes disadvantageous. One critical factor that contributes to the perceived success of taste matching is how much experience customers already accumulated within a consumption domain. More experienced individuals tend to develop more refined tastes and see their own tastes as idiosyncratic. As a result, it is more difficult for them to believe that their tastes can be accurately matched with other people’s tastes based on a single focal item. Another critical factor is the presence of other users’ profiles. Companies sometimes display the information of other users who are interested in the recommendation (e.g., books of “teens’ choice”), but this information backfires when it indicates to customers that they are different from other users. We show that dissimilarity cues, such as age and gender, make people infer that their tastes diverge from other users and lead to customers avoiding the user-based recommendations.
These novel findings have relevance for companies that use product recommendations to engage customers. Our research suggests that the explanation matters for why customers see a recommendation. Importantly, adapting the explanation that companies provide with a recommendation comes with almost zero cost and, thus, constitutes an effective tool that can help companies maximize the return on their investments in recommender systems. Importantly, we highlight the situations in which user-based framing is more effective than item-based framing and in which situations it becomes disadvantageous. By leveraging these findings, managers can tailor the framing of their recommendations for different customers and products and thereby boost click-through rates.
From: Phyliss Jia Gai and Anne-Kathrin Klesse, “Making Recommendations More Effective Through Framings: Impacts of User- Versus Item-Based Framings on Recommendation Click-Throughs,” Journal of Marketing, 83 (November).
Go to the Journal of Marketing